more fixes from pylint

.pylintrc

@@ -68,7 +68,7 @@ ignored-modules=sentencepiece.*,comfy.api,comfy.cmd.folder_paths
 
 # Python code to execute, usually for sys.path manipulation such as
 # pygtk.require().
-#init-hook=
+init-hook='import sys; sys.path.insert(0, ".")'
 
 # Use multiple processes to speed up Pylint. Specifying 0 will auto-detect the
 # number of processors available to use, and will cap the count on Windows to

comfy/app/app_settings.py

@@ -17,14 +17,14 @@ class AppSettings():
                 "comfy.settings.json"
             )
         except KeyError as e:
-            loggererror("User settings not found.")
+            logger.error("User settings not found.")
             raise web.HTTPUnauthorized() from e
         if os.path.isfile(file):
             try:
                 with open(file) as f:
                     return json.load(f)
             except:
-                loggererror(f"The user settings file is corrupted: {file}")
+                logger.error(f"The user settings file is corrupted: {file}")
                 return {}
         else:
             return {}

comfy/k_diffusion/sampling.py

@@ -1405,7 +1405,7 @@ def sample_gradient_estimation(model, x, sigmas, extra_args=None, callback=None,
 
     if cfg_pp:
         model_options = extra_args.get("model_options", {}).copy()
-        extra_args["model_options"] = comfy.model_patcher.set_model_options_post_cfg_function(model_options, post_cfg_function, disable_cfg1_optimization=True)
+        extra_args["model_options"] = model_patcher.set_model_options_post_cfg_function(model_options, post_cfg_function, disable_cfg1_optimization=True)
 
     for i in trange(len(sigmas) - 1, disable=disable):
         denoised = model(x, sigmas[i] * s_in, **extra_args)

comfy/ldm/ace/vae/music_vocoder.py

@@ -333,9 +333,9 @@ class ResBlock1(torch.nn.Module):
 
     def remove_weight_norm(self):
         for conv in self.convs1:
-            remove_weight_norm(conv)
+            remove_weight_norm(conv, None)
         for conv in self.convs2:
-            remove_weight_norm(conv)
+            remove_weight_norm(conv, None)
 
 
 class HiFiGANGenerator(nn.Module):
@@ -454,11 +454,11 @@ class HiFiGANGenerator(nn.Module):
 
     def remove_weight_norm(self):
         for up in self.ups:
-            remove_weight_norm(up)
+            remove_weight_norm(up, None)
         for block in self.resblocks:
             block.remove_weight_norm()
-        remove_weight_norm(self.conv_pre)
+        remove_weight_norm(self.conv_pre, None)
-        remove_weight_norm(self.conv_post)
+        remove_weight_norm(self.conv_post, None)
 
 
 class ADaMoSHiFiGANV1(nn.Module):

comfy/ldm/chroma/model.py

@@ -220,7 +220,7 @@ class Chroma(nn.Module):
             if i not in self.skip_dit:
                 single_mod = self.get_modulations(mod_vectors, "single", idx=i)
                 if ("single_block", i) in blocks_replace:
-                    def block_wrap(args):
+                    def block_wrap_1(args):
                         out = {}
                         out["img"] = block(args["img"],
                                            vec=args["vec"],
@@ -232,7 +232,7 @@ class Chroma(nn.Module):
                                                                "vec": single_mod,
                                                                "pe": pe,
                                                                "attn_mask": attn_mask},
-                                                              {"original_block": block_wrap})
+                                                              {"original_block": block_wrap_1})
                     img = out["img"]
                 else:
                     img = block(img, vec=single_mod, pe=pe, attn_mask=attn_mask)

comfy/ldm/modules/attention.py

@@ -21,10 +21,12 @@ if model_management.sage_attention_enabled():
         from sageattention import sageattn  # pylint: disable=import-error
     except ModuleNotFoundError as e:
         if e.name == "sageattention":
+            import sys
             logging.error(f"\n\nTo use the `--use-sage-attention` feature, the `sageattention` package must be installed first.\ncommand:\n\t{sys.executable} -m pip install sageattention")
         else:
             raise e
-        sageattn = torch.nn.functional.scaled_dot_product_attention
+else:
+    sageattn = torch.nn.functional.scaled_dot_product_attention
 
 
 if model_management.flash_attention_enabled():

comfy/weight_adapter/glora.py

@@ -54,6 +54,8 @@ class GLoRAAdapter(WeightAdapterBase):
         dora_scale = v[5]
 
         old_glora = False
+        # will correctly through div by zero error if rank was not read
+        rank = 0
         if v[3].shape[1] == v[2].shape[0] == v[0].shape[0] == v[1].shape[1]:
             rank = v[0].shape[0]
             old_glora = True

comfy/weight_adapter/lora.py

@@ -34,6 +34,8 @@ class LoRAAdapter(WeightAdapterBase):
         mochi_lora = "{}.lora_B".format(x)
         transformers_lora = "{}.lora_linear_layer.up.weight".format(x)
         A_name = None
+        B_name = None
+        mid_name = None
 
         if regular_lora in lora.keys():
             A_name = regular_lora

comfy_extras/nodes/nodes_hunyuan.py

@@ -108,6 +108,8 @@ class HunyuanImageToVideo:
                 out_latent["noise_mask"] = mask
             elif guidance_type == "custom":
                 cond = {"ref_latent": concat_latent_image}
+            else:
+                cond = None
 
             positive = node_helpers.conditioning_set_values(positive, cond)
 

comfy_extras/nodes/nodes_torch_compile.py

@@ -199,7 +199,7 @@ class QuantizeModel(CustomNode):
             if "autoquant" in strategy:
                 _in_place_fixme = autoquant(unet, error_on_unseen=False)
             else:
-                quantize_(unet, int8_dynamic_activation_int8_weight(), device=model_management.get_torch_device(), set_inductor_config=False)
+                quantize_(unet, int8_dynamic_activation_int8_weight(), device=model_management.get_torch_device())
                 _in_place_fixme = unet
             unwrap_tensor_subclass(_in_place_fixme)
         else: